The objective functions used in spectral clustering are usually composed of two terms: i) a term that minimizes the local quadratic variation of the cluster assignments on the graph and; ii) a term that balances the clustering partition and helps avoiding degenerate solutions. This paper shows that a graph neural network, equipped with suitable message passing layers, can generate good cluster assignments by optimizing only a balancing term. Results on attributed graph datasets show the effectiveness of the proposed approach in terms of clustering performance and computation time.
翻译:光谱集群中使用的目标功能通常由两个术语组成:(一) 尽可能减少图中组群任务本地四象形变化的术语;(二) 平衡组群分隔和帮助避免退化解决方案的术语。本文表明,配有适当信息传递层的图形神经网络可以通过优化平衡术语产生良好的组群任务。图表数据集上的成果显示了拟议方法在组合性能和计算时间方面的有效性。